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from typing import Dict, List, Any |
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from transformers import AutoTokenizer |
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig |
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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{} |
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### Input: |
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{} |
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### Response: |
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{}""" |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.model = AutoModelForCausalLM.from_pretrained(path, load_in_4bit=True) |
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self.tokenizer = AutoTokenizer.from_pretrained(path) |
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]: |
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sentence = data.pop("inputs",data).lower() |
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instruction_prompt = data.pop('prompt', data) |
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max_new_tokens = data.pop('max_new_tokens', data) |
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top_p = data.pop('top_p', data) |
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temperature = data.pop('temperature', data) |
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inputs = self.tokenizer( |
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[ |
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alpaca_prompt.format( |
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instruction_prompt, |
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sentence, |
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"", |
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) |
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], return_tensors="pt") |
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inputs = inputs.to('cuda') |
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outputs = self.model.generate(**inputs, |
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max_new_tokens=max_new_tokens, |
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top_p=top_p, |
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temperature=temperature) |
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outputs = self.tokenizer.batch_decode(outputs)[0] |
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response = outputs.split("### Response:")[1].split("<|end_of_text|>")[0] |
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return [{"generated_text": response}] |